SCI和EI收录∣中国化工学会会刊

›› 2009, Vol. 17 ›› Issue (1): 95-99.

• • 上一篇    下一篇

Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine

李修亮, 苏宏业, 褚健   

  1. National Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
  • 收稿日期:2007-08-12 修回日期:2008-10-14 出版日期:2009-01-28 发布日期:2009-01-28
  • 通讯作者: SU Hongye, E-mail: hysu@iipc.zju.edu.cn
  • 基金资助:
    Supported by the National High Technology Research and Development Program of China (2006AA040309);National BasicResearch Program of China (2007CB714000)

Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine

LI Xiuliang, SU Hongye, CHU Jian   

  1. National Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
  • Received:2007-08-12 Revised:2008-10-14 Online:2009-01-28 Published:2009-01-28
  • Supported by:
    Supported by the National High Technology Research and Development Program of China (2006AA040309);National BasicResearch Program of China (2007CB714000)

摘要: Presented is a multiple model soft sensing method based on Affinity Propagation(AP),Gaussian process(GP) and Bayesian committee machine(BCM).AP clustering arithmetic is used to cluster training samples according to their operating points.Then,the sub-models are estimated by Gaussian Process Regression(GPR).Finally,in order to get a global probabilistic prediction,Bayesian committee machine is used to combine the outputs of the sub-estimators.The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators.Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.

关键词: multiple model, soft sensor, affinity propagation, Gaussian process, Bayesian committee machine

Abstract: Presented is a multiple model soft sensing method based on Affinity Propagation(AP), Gaussian process(GP) and Bayesian committee machine(BCM).AP clustering arithmetic is used to cluster training samples according to their operating points.Then, the sub-models are estimated by Gaussian Process Regression(GPR).Finally, in order to get a global probabilistic prediction, Bayesian committee machine is used to combine the outputs of the sub-estimators.The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators.Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.

Key words: multiple model, soft sensor, affinity propagation, Gaussian process, Bayesian committee machine